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What is the relationship between DX and AI? Three points when using AI to promote DX

Some people may have a strong image of “doing something new with AI” when it comes to DX (digital transformation). It is true that there are many cases where AI is used in actual DX, but “promotion of DX” and “introduction of AI” are not equal.

Therefore, in this article, after reorganizing what AI is, I will explain the relationship with DX promotion and the points for using AI effectively. We will also touch on securing human resources for the introduction of AI.

What is AI

What the term “AI” (artificial intelligence) refers to is not clearly defined. First, let’s organize what AI means in the context of “using it for DX” from the following perspectives.

– Goals of AI
– Automate improvement of model accuracy with machine learning
– Expanding business applications with deep learning

What AI aims for

If we were to describe AI in one word, we would say that it aims to be a “computer that thinks like humans.”

Computers are good at performing complex calculations accurately and processing large amounts of data at high speed. Their abilities far exceed those of humans, and the use of IT has improved various operations. Furthermore, with the addition of technologies such as RPA, not only efficiency but also automation is progressing.

On the other hand, it is not easy to make a computer “think” like a human brain. AI must first “learn” from existing data and rules. Then, you will be able to draw new conclusions by “inference” based on the learned knowledge.

AI is a model that reproduces human thought on a computer by “modeling” human thinking using such a mechanism. AI may be able to automate tasks that would otherwise require human judgment.

Automate model accuracy improvement with machine learning

AI generally becomes able to make appropriate inferences only by giving it a large amount of data. “Machine learning” is a useful means for making AI with such properties learn efficiently.

Machine learning can automate the process of AI learning from data. This means that AI will learn by itself and will be a means to gradually improve the accuracy of the model. At this time, the ability to prepare sufficient quality data is an important factor that determines the outcome of learning.

There is an article that explains in detail about machine learning, so please refer to it as well.

Expanding business utilization with deep learning

Machine learning is realized by many knowledge including mathematics and statistics. Among them, a technology called “deep learning” is positioned as an important element.

Deep learning has been researched and developed since it came into the limelight in the field of image recognition in 2012. By mimicking the structure of neurons in the human brain, it is characterized by being able to flexibly learn even from complex data. It is a breakthrough in AI technology because it can be applied to fields that were previously difficult, such as speech and natural language analysis.

At present, the environment for using deep learning in system development is also in place. In DX, which makes the most of digital technology and data, AI that incorporates deep learning has come to be widely used in business. For example, in combination with RPA, etc., it is possible to aim for automation based on more advanced judgments.

We also have a detailed explanation of AI utilization examples in this article , so please refer to it as well.

Relationship between DX and AI

DX is “continuing to create an organization and culture that can add value to customers by utilizing digital technology”. Please refer to this article for a more detailed explanation of this definition .

Here, let’s take a closer look at the positioning of AI in DX.

– Differences between DX and AI
– AI is one of the digital technologies that support the realization of corporate transformation
– Benefits increase when combined with IoT and big data

Difference between DX and AI

It is true that many of the successful cases of DX make effective use of AI. However, there is a problem in equating “promotion of DX” with “introduction of AI”. If I were to express the difference between the two in one word, AI would be the “means” to achieve it, when DX is the “purpose”.

If we do not recognize these differences, the introduction of AI may end up becoming a goal. In that case, there is a risk that DX, which should be the original purpose, will be neglected. In fact, this is one of the common failure patterns in promoting DX.

AI is one of the digital technologies that support the realization of corporate transformation

The essence of DX is to strengthen and maintain competitiveness as a company even in the current business environment, which continues to change rapidly due to advanced digitalization. To that end, continuous efforts are required to transform the whole, including the organization and culture.

The key here is the use of digital technology and data. At this time, AI is just one option for digital technology that can be used for DX. However, AI has a wide range of uses and can be used for various purposes, so there are many examples of its use in DX.

Benefits increase when combined with IoT and big data

Modern business is largely supported by information and communication technology (ICT). In recent years, high-speed communication by 5G has been put to practical use, and it has become commonplace for data to flow everywhere. For companies, this means that an environment has been created in which it is easy to collect and retain various data related to business.

AI is characterized by being able to make high-value inferences by properly learning from a large amount of data. In addition to data on business execution and quality drawn from internal processes, IoT and big data are also “sources” for AI learning. It can be said that the value of the data held by companies has increased due to the practical application of AI.

Depending on how AI is used, for example, the following benefits can be obtained.

– Cost reduction
– Reduction of quality unevenness
– Process improvement and productivity improvement
– ​​Application to new services

There is an article that explains the benefits of introducing AI in detail , so please refer to it as well.

Examples of Japanese companies that have introduced AI technology to DX

Examples of Japanese companies that have introduced AI technology to DX

AI has a wide range of applications, so it is being adopted by many companies across industries and industries. From here, we will introduce the following four usage examples that companies that are considering introducing AI can easily refer to.

– Significantly reduce R&D costs by utilizing data
– Maintain quality by detecting defective products using images
– Improve productivity by reducing simple work with RPA
– Reduce call center response time with voice recognition

Significant reduction in research and development costs through data utilization

The purpose of having AI learn is to discover some kind of pattern hidden in various data. This means that it is possible to discover new knowledge even from the existing data owned by companies.

For example, pharmaceutical companies have accumulated a large amount of data on past experiments, research, and the characteristics of various drugs. By using them as learning data, it is also possible to build AI for the purpose of optimizing the new drug development process. In fact, many pharmaceutical companies have started such efforts. There have also already been reports of significant cost savings.

Maintaining quality with image-based defect detection

In the manufacturing industry, it is normal to have a certain cost to maintain quality. For example, in factories that manufacture and process food, there are many cases where visual inspection is performed.

There is an example of automating such checks by the “human eye” by introducing AI. By learning in advance from image data of “good” and “defective” products, a system can be constructed that automatically distinguishes between the two. Of course, the scope of application of these systems is not limited to inspection. Automation using a similar method will also be useful for checking for defective raw materials.

Reduce simple work and improve productivity with RPA

Reduce simple work and improve productivity with RPA

Combining AI and RPA has the potential to make a wider range of operations more efficient. RPA stands for “Robotic Process Automation”. If we add the judgment power of AI to this, further automation will be possible.

For example, in departments such as human resources, general affairs, and accounting, there are many tasks that are difficult to manual even though they are basically simple tasks. Automating these tasks has traditionally been a difficult task.

However, in recent years, we have begun to see examples of successful automation by having AI learn the business content. In actual cases, it is said that not only productivity improved by reducing the time spent on simple work, but also the psychological stress of workers was reduced.

Reduce call center response time with voice recognition

Call centers generally have a high turnover rate of operators, and there are many cases where a shortage of human resources is an issue. On the other hand, veteran skills are also necessary for speedy and accurate responses.

Therefore, there is an example of digitizing the telephone manual and combining it with voice recognition by AI. AI automatically recognizes the content of the conversation and presents corresponding candidate answers to the operator. This shortened the time required to find the right answer and enabled speedy response. In addition, it is said that it was possible to reduce the difference in response quality due to the ability of the operator.

Three points when using AI to promote DX

In order to effectively use AI in promoting DX, there are points to be kept in mind. Here, we will discuss the following three points.

– Point 1: Make DX compatible with management strategy
– Point 2: Secure human resources capable of building IT systems
– Point 3: Increase human resources through in-house training

Point 1: Make DX correspond to management strategy

As long as it is an initiative related to competitiveness as a company, it is important that DX is consistent with management strategy. It can be said that the point is whether the purpose and expected effects of introducing AI are linked to the company’s mission, vision, market needs, etc.

However, in some cases a lack of technical knowledge makes this difficult. The challenge will be how to bridge the gap between technology and management and establish a system that enables constructive discussions. One way to deal with these issues is to use external consultants to get an objective perspective.

We have prepared a service for that purpose , so please take advantage of it.

Point 2: Secure human resources who can build IT systems

In order to make full use of AI, in addition to machine learning theory including deep learning, basic knowledge of mathematics and statistics, skills related to specialized tools, etc. are required. It may not be easy to secure personnel with such a wide range of knowledge and skills. Even if it is clear what you want to achieve with AI, there may be cases where DX cannot proceed due to the inability to secure resources.

Therefore, there is an option to outsource the development. However, it cannot be said that there are no problems if you ask a vendor with extensive experience in AI and DX. For example, the developed system may turn into a black box, hindering its continued use in DX.

When choosing a vendor, it should be based on whether or not they can provide support for “in-house production” without hiding their technology and know-how. Please consider our service as one of the options for securing development resources .

Point 3: Increase human resources through in-house training

As long as DX is an ongoing effort, I would like to consider training AI personnel in-house. Even if it is difficult in the immediate future, it is possible to develop human resources with AI knowledge and skills from a long-term perspective. For example, there is a method of starting with outsourced development and gradually absorbing know-how toward in-house production.

Keep in mind that in AI human resource development, the presence of managers is important in addition to the engineers directly involved in development. This is because the management of AI projects requires ideas and methods that are unique to AI. In order to make DX successful, it is recommended to make a plan that can train both engineers and managers who know AI.

We provide training services for this purpose. It is a course that focuses on “super practical and practical perspectives” and supports the acquisition of reliable knowledge, so please take advantage of it.

Securing human resources is essential if AI is to be introduced into DX

AI is a technology that can be applied to streamline and automate various operations. It is widely recognized as one of the important elements that support DX because it can make the most of the data owned by companies.

On the other hand, using AI requires a wide range of specialized knowledge and skills. Consulting services and outsourced development should be used as necessary to build strategies and secure human resources for the realization of DX. At this time, in order to accumulate know-how in the company, it is recommended to plan for AI human resource development.

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